Irregularities in railway tracks are a key factor influencing the safety of trains. In this paper, rail track is considered to consist of consecutive track maintenance units whose individual defect states can be quantified in terms of a track quality index. A Markov stochastic process approach is used to evaluate the deterioration of a maintenance unit. A hazard model is formulated using the heterogeneity of the maintenance units, and a matrix of the Markov transition probabilities is constructed. The parameters of the developed models are estimated via a maximum log-likelihood function. The prediction model is validated with track irregularity data measured using track geometry cars.
SatoYXuYLiD. New track mechanics, Beijing, People’s Republic of China: China Railway Publishing House, 2001.
2.
International Union of Railways. Dynamic vehicle/track interaction phenomena from the point of view of track maintenance. Railway Technical Publications, D 161, 1988.
3.
KawaguchiAMiwaMTeradaK. Actual data analysis of alignment irregularity growth and its prediction model. Q R RTRI2005; 46: 262–268.
4.
Veit P and Marschnig S. Sustainability in Track: A Precondition for High Speed Traffic. In: Proceedings of the 2010 Joint Rail Conference, Urbana, Illinois, USA, 2010. New York: ASME, pp.349–355.
5.
QuirogaLMSchniederEA heuristic approach to railway track maintenance scheduling. In: NingBBrebbiaCA (eds). Computers in railways XII: computer system design and operation in railways and other transit systems, Southampton, UK: Wit Press, 2010, pp. 687–699.
6.
Jovanovic S. Railway track quality assessment and related decision making. In: The 2004 IEEE international conference on systems, man and cybernetics, The Hague, The Netherlands, 2004, volume 6, pp.5038–5043. Piscataway, NJ: IEEE Press.
7.
Xu P, Sun Q, Liu R and Wang F. Key equipment identification model for correcting milepost errors of track geometry data from track inspection cars. Transport Res, Part C: Emerg Technol 2013; 35: 85–103.
8.
Xu P, Sun Q, Liu R, et al. Optimizing the Alignment of Inspection Data from Track Geometry Cars. Comput-Aided Civil Infrastruct Engng forthcoming.
9.
XuPSunQLiuRWangF. A short-range prediction model for track quality index. Proc IMechE, Part F: J Rail Rapid Transit2011; 225: 277–285.
10.
LiuRXuPWangF. Research on a short-range prediction model for track irregularity over small track lengths. J Transport Engng2010; 136: 1085–1091.
11.
Meier-HirmerCSeneeARibouletGA decision support system for track maintenance. In: AllanJBrebbiaCARumseyAFSciuttoGSoneSGoodmanCJ (eds). Computers in railways X: computer system design and operation in the railway and other transit systems, Southampton, UK: Computational Mechanics Publications Ltd, 2006, pp. 217–226.
12.
Meier-HirmerCRibouletGSourgetFRoussignolM. Maintenance optimization for a system with a gamma deterioration process and intervention delay: application to track maintenance. Proc IMechE, Part O:J Risk Reliab2009; 223: 189–198.
13.
MishalaniRMadanatS. Computation of infrastructure transition probabilities using stochastic duration models. J Infrastruct Syst2002; 8: 139–148.
14.
KobayashiKKaitoKLethanhN. Deterioration forecasting model with multistage Weibull hazard functions. J Infrastruct Syst2010; 16: 282–291.
LeeTCJudgeGGZellnerA. Estimating the parameters of the Markov probability model from aggregate time series data, Amsterdam, The Netherlands: North-Holland, 1970.
17.
KobayashiKDoMHanD. Estimation of Markovian transition probabilities for pavement deterioration forecasting. KSCE J Civil Engng2010; 14: 343–351.
18.
Madanat SM and Mishalani RG. Infrastructure state transition probability computation using duration models. In: The seventh international conference on applications of advanced technologies in transportation (eds KCP Wang, S Madanat,S Nambisan and S Spring), Boston, MA, 5–7 August 2002, pp. 505–512. Reston, VA: ASCE Press.
GolabiKShepardR. Pontis: a system for maintenance optimization and improvement of US bridge networks. Interfaces1997; 27: 71–88.
21.
KobayashiKKaitoKLethanhN. A statistical deterioration forecasting method using hidden Markov model for infrastructure management. Transport Res, Part B: Methodol2012; 46: 544–561.
22.
The Railway Ministry of People's Republic of China. Transport base line, 2009. No. 41. Track quality index management interim standards of existing railway, Beijing, People’s Republic of China: China Railway Publishing House, 2009.
23.
The Railway Ministry of People's Republic of China. Railway Transport,2006. No. 41. Rules of railway track maintenance, Beijing, People’s Republic of China: China Railway Publishing House, 2010.
24.
Encyclopedia Committee of China Railway. Encyclopaedia of China railways engineering & railway maintenance, Beijing, People’s Republic of China: China Railway Publishing House, 2004.
25.
LuoLZhangGWuWChaiX. The control of track irregularity state in wheel-rail system, Beijing, People’s Republic of China: China Railway Publishing House, 2006.
26.
Bartram D, Burrow M and Yao X. A Computational Intelligence Approach to Railway Track Intervention Planning. In: Yu T, Davis L, Baydar C, Roy R (eds) Evolutionary Computation in Practice. Heidelberg, Berlin: Springer, 2008, pp.163–198.
27.
Dell'OrcoMOttomanelliMCaggianiLSassanelliD. New decision support system for optimization of rail track maintenance planning based on adaptive neurofuzzy inference system. Transport Res Record: J Transport Res Board2008; 2043: 49–54.
28.
LancasterT. The econometric analysis of transition data, Cambridge, UK: Cambridge University Press, 1992.
